Abstract
This paper describes a new visual feature to especially address the problem of highly dynamic place recognition. The feature is obtained by identifying existing local features, such as SIFT or SURF, that have wide baseline visibility within the place. These identified local features are then compressed into a single representative feature, a wide-baseline visible feature, which is computed as an average of all the features associated with it. The proposed feature is especially robust against highly dynamical changes in scene; it can be correctly matched against a number of features collected from many dynamic images. This paper also describes an approach to using these features for scene recognition. The recognition proceeds by matching individual feature to a set of features from testing images, followed by majority voting to identify a place with the highest matched features. The proposed feature is trained and tested on 2000+ outdoor omnidirectional. Despite its simplicity, wide-baseline visible feature offers two times better rate of recognition (ca. 93%) than other features. The number of features can be further reduced to speed up the time without dropping in accuracy, which makes it more suitable to long-term scene recognition and localization.
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Kawewong, A., Tangruamsub, S., Hasegawa, O. (2009). Wide-Baseline Visible Features for Highly Dynamic Scene Recognition. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_88
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DOI: https://doi.org/10.1007/978-3-642-03767-2_88
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